Prediction of the Second Transition Point of Tool Wear Phase Using Vibratory Signal Analysis (ZROT)


  • Dr. Nur Adilla Kasim Politeknik Mukah
  • Ts. Mohd Ghafran Mohamed Politeknik Mukah
  • Dr. Mohd Zaki Nuawi Universiti Kebangsaan Malaysia


Cutting Tool Wear, Signal Analysis, Z-rot, Piezofilm-Based Sensor, Vibration


Early intervention to change worn cutting tool before its failure could avoid unexpected machine downtime. A mathematical based predictive model is employed to estimate early tool failure using vibratory signal. The statistical based signal analysis technique as wear tracking analysis is applied in the predictive model to outline the data pattern concerning wear and number of cutting. The signal analysis based on the changes in the vibration signatures that captured from accelerometer during the milling operation throughout the tool life. A significant correlation between the tool flank wear and the statistical index has achieved. The tool life as a function of the acceleration amplitude of assimilated vibrations. Selected curve fitting equations are considered to decide the transition point between the steady state and failure region. The result shows a significant expectation of determining the second transition point with estimate value of 0.235mm below the rapid wear (<0.25mm). The accuracy, reliability and robustness of the predicted transition point were then parallel against another sensing elements where it predicts almost the same transition point. The de-termination of the second transition point will assist the preparation to anticipate the tool to be bro-ken. The results reflected that the model gives reasonable estimation of tool life and the transition points at which changes of the region transpire.

Author Biographies

Dr. Nur Adilla Kasim, Politeknik Mukah

Department of Mechanical Engineering (Lecturer)

Ts. Mohd Ghafran Mohamed, Politeknik Mukah

Department of Mechanical Engineering (Lecturer)

Dr. Mohd Zaki Nuawi, Universiti Kebangsaan Malaysia

Department of Mechanical and Manufacturing Engineering (Lecturer)